May 1, 2024
3 minute read
Probabilistic models are mathematical representations of random phenomena. They are used to describe the probability of an event occurring, and to make predictions about future events. Probabilistic models are used in a wide variety of fields, including:
Statistics
Statistics is the science of collecting, organizing, and interpreting data. Probabilistic models are used in statistics to make inferences about a population based on a sample. For example, a pollster might use a probabilistic model to estimate the percentage of people in a country who will vote for a particular candidate.
Predictive Analytics
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Find a path to becoming a Probabilistic Models. Learn more at:
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Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Probabilistic Models.
This comprehensive textbook provides a thorough grounding in the theory and applications of probabilistic graphical models, covering topics such as Bayesian networks, Markov random fields, and Kalman filters. It is an excellent resource for students and researchers in machine learning, artificial intelligence, and other fields that use probabilistic models.
Focuses on the application of probabilistic models to robotics, covering topics such as localization, mapping, and motion planning. It valuable resource for researchers and practitioners in the field of robotics.
Provides a comprehensive introduction to probabilistic models, covering topics such as measure theory, probability theory, and random processes. It is an excellent resource for students and researchers in probability theory and related fields.
Provides a comprehensive treatment of probabilistic graphical models, covering topics such as Bayesian networks, Markov random fields, and Kalman filters. It is an excellent resource for students and researchers in machine learning, artificial intelligence, and other fields that use probabilistic models.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering topics such as supervised learning, unsupervised learning, and Bayesian inference. It is an excellent resource for students and researchers in machine learning and related fields.
Provides a comprehensive treatment of Gaussian processes, which are a powerful class of probabilistic models that have been widely used in machine learning. It covers topics such as regression, classification, and Bayesian optimization.
Provides a comprehensive introduction to graphical models for machine learning and data mining, covering topics such as Bayesian networks, Markov random fields, and Kalman filters. It is an excellent resource for students and researchers in machine learning, artificial intelligence, and other fields that use probabilistic models.
While this book does not focus exclusively on probabilistic models, it provides a comprehensive introduction to statistical learning methods, many of which are based on probabilistic models. It valuable resource for students and researchers in machine learning and related fields.
Provides a comprehensive introduction to computational statistics, covering topics such as Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian inference. It is an excellent resource for students and researchers in statistics and related fields.
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and Bayesian inference. It is an excellent resource for students and researchers in machine learning and related fields.
Provides a comprehensive introduction to probabilistic models for data analysis, covering topics such as Bayesian inference, model selection, and predictive modeling. It is an excellent resource for students and researchers in statistics and related fields.
Provides a comprehensive introduction to causal inference in statistics, covering topics such as graphical models, causal effects, and counterfactuals. It is an excellent resource for students and researchers in statistics, machine learning, and other fields that use probabilistic models.
Provides a comprehensive introduction to Bayesian data analysis, covering topics such as Bayesian inference, model checking, and predictive modeling. It is an excellent resource for students and researchers in statistics and related fields.
Provides a non-technical introduction to causal inference, covering topics such as graphical models, causal effects, and counterfactuals. It is an excellent resource for anyone who wants to learn more about the science of cause and effect.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/bq139c/probabilistic
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